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Artificial intelligence | MIT News | Massachusetts Institute of Technology The philosophical puzzle of rational artificial intelligence As AI technology advances, a new interdisciplinary course seeks to equip students with foundational critical thinking skills in computing January 30, 2026 Read full story
What does the future hold for generative AI? - MIT News Hundreds of scientists, business leaders, faculty, and students shared the latest research and discussed the potential future course of generative AI advancements during the inaugural symposium of the MIT Generative AI Impact Consortium (MGAIC) on Sept 17
What do we know about the economics of AI? - MIT News Since much economic growth comes from tech innovation, the way societies use artificial intelligence is of keen interest to MIT Institute Professor Daron Acemoglu, who has published several papers on AI economics in recent months
AI tool generates high-quality images faster than state-of-the-art . . . A hybrid AI approach known as hybrid autoregressive transformer can generate realistic images with the same or better quality than state-of-the-art diffusion models, but that runs about nine times faster and uses fewer computational resources The new tool uses an autoregressive model to quickly capture the big picture and then a small diffusion model to refine the details of the image
MIT researchers develop an efficient way to train more reliable AI . . . MIT researchers developed an efficient approach for training more reliable reinforcement learning models, focusing on complex tasks that involve variability This could enable the leverage of reinforcement learning across a wide range of applications
“Periodic table of machine learning” could fuel AI discovery After uncovering a unifying algorithm that links more than 20 common machine-learning approaches, MIT researchers organized them into a “periodic table of machine learning” that can help scientists combine elements of different methods to improve algorithms or create new ones
MIT researchers advance automated interpretability in AI models MAIA is a multimodal agent for neural network interpretability tasks developed at MIT CSAIL It uses a vision-language model as a backbone and equips it with tools for experimenting on other AI systems
Responding to the climate impact of generative AI - MIT News MIT experts discuss strategies and innovations aimed at mitigating the amount of greenhouse gas emissions generated by the training, deployment, and use of AI systems, in the second in a two-part series on the environmental impacts of generative artificial intelligence
Introducing the MIT Generative AI Impact Consortium The MIT Generative AI Impact Consortium is a collaboration between MIT, founding member companies, and researchers across disciplines who aim to develop open-source generative AI solutions, accelerating innovations in education, research, and industry